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1 Jean Daunizeau Wellcome Trust Centre for Neuroimaging 08 / 05 / 2009 EEG-MEG source reconstruction rIFG rSTG rA1 lSTG lA1.

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Presentation on theme: "1 Jean Daunizeau Wellcome Trust Centre for Neuroimaging 08 / 05 / 2009 EEG-MEG source reconstruction rIFG rSTG rA1 lSTG lA1."— Presentation transcript:

1 1 Jean Daunizeau Wellcome Trust Centre for Neuroimaging 08 / 05 / 2009 EEG-MEG source reconstruction rIFG rSTG rA1 lSTG lA1

2 2 EEG/MEG data baseline correction averaging over trials low pass filter (20Hz) trials data convert epoching sensor locations inverse modelling 1st level contrast standard SPM analysis gain matrix individual meshes evoked responses cortical sources spatial denormalisation anatomical templates structural MRI BEM forward modelling

3 3 EEG/MEG data baseline correction averaging over trials low pass filter (20Hz) trials data convert epoching sensor locations inverse modelling 1st level contrast gain matrix evoked responses anatomical templates standard SPM analysis individual meshes cortical sources spatial denormalisation structural MRI BEM forward modelling

4 4 1.Introduction 2.Forward problem 3.Inverse problem 4.Bayesian inference applied to distributed source reconstruction 5.SPM variants of the EEG/MEG inverse problem 6.Conclusion BayesSPMConclusionInverseForward Introduction

5 5 Forward problem = modelling Inverse problem = estimation of the model parameters BayesInverseForward Introduction Forward and inverse problems: definitions SPMConclusion

6 6 current dipole BayesInverseForward Introduction Physical model of bioelectrical activity SPMConclusion

7 7 measurements noise dipoles gain matrix Y = KJ + E 1 BayesInverseForward Introduction Fields propagation through head tissues SPMConclusion

8 8 Jacques Hadamard ( ) 1.Existence 2.Unicity 3.Stability BayesForward Introduction An ill-posed problem InverseSPMConclusion

9 9 Jacques Hadamard ( ) 1.Existence 2.Unicity 3.Stability BayesForward Introduction An ill-posed problem InverseSPMConclusion

10 10 BayesForward Introduction Imaging solution: cortically distributed dipoles InverseSPMConclusion

11 11 BayesForward Introduction Imaging solution: cortically distributed dipoles InverseSPMConclusion

12 12 Data fit Adequacy with other modalities Spatial and temporal constraints W = I : minimum norm method W = Δ : LORETA (maximum smoothness) data fitconstraint (regularization term) BayesForward Introduction Regularization InverseSPMConclusion

13 13 likelihoodpriors posterior model evidence Forward Introduction Priors and posterior InverseBayesSPMConclusion

14 14 sensor level source level Q : (known) variance components (λ,μ) : (unknown) hyperparameters Forward Introduction Hierarchical generative model InverseBayesSPMConclusion

15 15 YJμ1μ1 μqμq λ1λ1 λqλq Forward Introduction Hierarchical generative model: graph InverseBayesSPMConclusion

16 16 generative model M average over J model associated with F Forward Introduction Restricted Maximum Likelihood (ReML) InverseBayesSPMConclusion

17 17 generative model M prior covariance structure IID COH ARD/GS Forward Introduction Imaging source reconstruction in SPM InverseBayesSPMConclusion

18 18 Source reconstruction for group studies canonical meshes! Forward Introduction Group studies InverseBayesSPMConclusion

19 19 EEG/MEG data measurement noise precision ECD positions ECD moments ECD moments prior precision ECD positions prior precision soft symmetry constraints! Somesthesic stimulation (evoked potential) Forward Introduction Equivalent Current Dipoles (ECD) InverseBayesSPMConclusion

20 20 ensemble (10 5 ~10 6 neurons) mean-field response (due to ensemble dispersion) effective connectivity (due to synaptic density) macroscopic scalemesoscopic scalemicroscopic scale excitatory interneurons pyramidal cells inhibitory interneurons system of ensemblesneuron Forward Introduction Dynamic Causal Modelling (DCM) InverseBayesSPMConclusion

21 21 Prior information is mandatory to solve the inverse problem. EEG/MEG source reconstruction: 1. forward problem; 2. inverse problem (ill-posed). Bayesian inference is well suited for: 1. introducing such prior information… 2. … and estimating their weight wrt the data 3. providing us with a quantitative feedback on the adequacy of the model. Forward Introduction InverseBayesSPMConclusion

22 22 R L individual reconstructions in MRI template space RFX analysis p < 0.01 uncorrected RL SPM machinery Forward Introduction InverseBayesSPMConclusion

23 23 Many thanks to… Karl Friston Stephan Kiebel Jeremie Mattout Christophe Phillips Vladimir Litvak Guillaume Magic Flandin Forward Introduction InverseBayesSPMConclusion


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